schain Files · schainpy/scripts/optimun_offset.py

Se realizar la lectura en modo online llamando al metodo digitalRFReader(self.path) en reemplazo del metodo reload(), grabando previamente el path de lectura o directorio superior donde se almacena la data. Adicionalmente, se ha definido un tiempo de espera de 3 segundos para dar tiempo suficiente al programa de adquisicion de generar archivos. El archivo jroIO_digitalRF.py utiliza la libreria digital_rf cuya version actual es la 2.62( 2017 ) ,esta libreria no tiene definido el metodo o clase reload, este metodo existe en la version 2.0(2014), si uno revisa el archivo jroIO_usrp.py, esta unidad de lectura trabaja con la version 2.0 llamada digital_rf_hdf5, para hacer uso de esta unidad de lectura se instalan los programas correspondiente pero el formato y la informacion difiere un poco de la version actual. Se infiere entonces que al desarrollar del archivo jroIO_digitalRF.py, esperaba que la libreria aun tenga incluido el metodo reload con el update de las versiones pero este ya no es parte del desarrollo, Se realizo la consulta al desarrollador actual de digitalRF Ryan Voltz si se iba a incluir a futuro pero indico que no era necesario.

José Chávez - - Load All Authors

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      import h5py

      import numpy

      import matplotlib.pyplot as plt

      import glob

      import os

      #----------------------    Functions     ---------------------

      def findFiles(path):

          dirList = []

          fileList = []

          for thisPath in os.listdir(path):

              dirList.append(os.path.join(path,thisPath))

              dirList.sort()

          for thisDirectory in dirList:

      		files = glob.glob1(thisDirectory, "*.hdf5")

      		files.sort()

      		for thisFile in files:

      			fileList.append(os.path.join(thisDirectory,thisFile))   

          return fileList

      def readFiles(fileList):

          meteors_array = numpy.zeros((1,4))

          for thisFile in fileList:

              #Leer

      		f1 = h5py.File(thisFile,'r')

      		grp1 = f1['Data']

      		grp2 = grp1['data_output']     

      		meteors1 = grp2['table0'][:]

      		meteors_array = numpy.vstack((meteors_array,meteors1))

              #cerrar

      		f1.close()

          meteors_array = numpy.delete(meteors_array, 0, axis=0)

          meteors_list = [meteors_array[:,0],meteors_array[:,1],meteors_array[:,2],meteors_array[:,3]]

          return meteors_list

      def estimateMean(offset_list):

          mean_off = []

          axisY_off = []

          axisX_off = []

          for thisOffset in offset_list:

      		mean_aux = numpy.mean(thisOffset, axis = 0)

      		mean_off.append(mean_aux)

      		axisX_off.append(numpy.array([0,numpy.size(thisOffset)]))

      		axisY_off.append(numpy.array([mean_aux,mean_aux]))

          return mean_off, axisY_off, axisX_off

      def plotPhases(offset0, axisY0, axisX0, title):

          f, axarr = plt.subplots(4, sharey=True)

          color = ['b','g','r','c']

      # plt.grid()

          for i in range(len(offset0)):

      		thisMeteor = offset0[i]

      		thisY = axisY0[i]

      		thisX = axisX0[i]

      		thisColor = color[i]

      		opt = thisColor + 'o'

      		axarr[i].plot(thisMeteor,opt)

      		axarr[i].plot(thisX, thisY, thisColor)

      		axarr[i].set_ylabel('Offset ' + str(i))

          plt.ylim((-180,180))

          axarr[0].set_title(title + ' Offsets')

          axarr[3].set_xlabel('Number of estimations')

          return

      def filterOffsets(offsets0, stdvLimit):

          offsets1 = []

          for thisOffset in offsets0:

              pstd = numpy.std(thisOffset)*stdvLimit

              pmean = numpy.mean(thisOffset)

              outlier1 = thisOffset > pmean - pstd

              outlier2 = thisOffset < pmean + pstd       

              not_outlier = numpy.logical_and(outlier1,outlier2)

              thisOffset1 = thisOffset[not_outlier]

              offsets1.append(thisOffset1)

          return offsets1

      #----------------------     Setup    ---------------------------

      <<<<<<< HEAD

      path = '/home/nanosat/Pictures/JASMET30_mp/201608/phase'

      =======

      path = '/home/jespinoza/Pictures/JASMET30/201608/phase'

      >>>>>>> master

      stdvLimit = 0.5

      #----------------------   Script     ---------------------------

      fileList = findFiles(path)

      offsets0 = readFiles(fileList)

      mean0, axisY0, axisX0 = estimateMean(offsets0)

      plotPhases(offsets0, axisY0, axisX0, 'Original')

      offsets1 = filterOffsets(offsets0, stdvLimit)

      mean1, axisY1, axisX1 = estimateMean(offsets1)

      plotPhases(offsets1, axisY1, axisX1, 'Filtered')

      print "Original Offsets: %.2f, %.2f, %.2f, %.2f" % (mean0[0],mean0[1],mean0[2],mean0[3])

      print "Filtered Offsets: %.2f, %.2f, %.2f, %.2f" % (mean1[0],mean1[1],mean1[2],mean1[3])

      plt.show()

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				import h5py
				import numpy
				import matplotlib.pyplot as plt
				import glob
				import os

				#---------------------- Functions ---------------------

				def findFiles(path):

				dirList = []
				fileList = []

				for thisPath in os.listdir(path):
				dirList.append(os.path.join(path,thisPath))
				dirList.sort()

				for thisDirectory in dirList:
				files = glob.glob1(thisDirectory, "*.hdf5")
				files.sort()
				for thisFile in files:
				fileList.append(os.path.join(thisDirectory,thisFile))

				return fileList

				def readFiles(fileList):

				meteors_array = numpy.zeros((1,4))

				for thisFile in fileList:

				#Leer
				f1 = h5py.File(thisFile,'r')
				grp1 = f1['Data']
				grp2 = grp1['data_output']
				meteors1 = grp2['table0'][:]
				meteors_array = numpy.vstack((meteors_array,meteors1))
				#cerrar
				f1.close()

				meteors_array = numpy.delete(meteors_array, 0, axis=0)
				meteors_list = [meteors_array[:,0],meteors_array[:,1],meteors_array[:,2],meteors_array[:,3]]
				return meteors_list

				def estimateMean(offset_list):

				mean_off = []
				axisY_off = []
				axisX_off = []

				for thisOffset in offset_list:
				mean_aux = numpy.mean(thisOffset, axis = 0)
				mean_off.append(mean_aux)
				axisX_off.append(numpy.array([0,numpy.size(thisOffset)]))
				axisY_off.append(numpy.array([mean_aux,mean_aux]))

				return mean_off, axisY_off, axisX_off

				def plotPhases(offset0, axisY0, axisX0, title):
				f, axarr = plt.subplots(4, sharey=True)
				color = ['b','g','r','c']
				# plt.grid()
				for i in range(len(offset0)):
				thisMeteor = offset0[i]
				thisY = axisY0[i]
				thisX = axisX0[i]
				thisColor = color[i]

				opt = thisColor + 'o'
				axarr[i].plot(thisMeteor,opt)
				axarr[i].plot(thisX, thisY, thisColor)
				axarr[i].set_ylabel('Offset ' + str(i))

				plt.ylim((-180,180))
				axarr[0].set_title(title + ' Offsets')
				axarr[3].set_xlabel('Number of estimations')

				return

				def filterOffsets(offsets0, stdvLimit):
				offsets1 = []

				for thisOffset in offsets0:
				pstd = numpy.std(thisOffset)*stdvLimit
				pmean = numpy.mean(thisOffset)
				outlier1 = thisOffset > pmean - pstd
				outlier2 = thisOffset < pmean + pstd
				not_outlier = numpy.logical_and(outlier1,outlier2)
				thisOffset1 = thisOffset[not_outlier]
				offsets1.append(thisOffset1)

				return offsets1

				#---------------------- Setup ---------------------------

				<<<<<<< HEAD
				path = '/home/nanosat/Pictures/JASMET30_mp/201608/phase'
				=======
				path = '/home/jespinoza/Pictures/JASMET30/201608/phase'
				>>>>>>> master
				stdvLimit = 0.5

				#---------------------- Script ---------------------------

				fileList = findFiles(path)
				offsets0 = readFiles(fileList)
				mean0, axisY0, axisX0 = estimateMean(offsets0)
				plotPhases(offsets0, axisY0, axisX0, 'Original')

				offsets1 = filterOffsets(offsets0, stdvLimit)
				mean1, axisY1, axisX1 = estimateMean(offsets1)
				plotPhases(offsets1, axisY1, axisX1, 'Filtered')

				print "Original Offsets: %.2f, %.2f, %.2f, %.2f" % (mean0[0],mean0[1],mean0[2],mean0[3])
				print "Filtered Offsets: %.2f, %.2f, %.2f, %.2f" % (mean1[0],mean1[1],mean1[2],mean1[3])

				plt.show()